Weighted round robin load balancing is a common routing policy offered in cloud load balancers. However, there is a lack of effective mechanisms to decide the weights assigned to each server to achieve an overall optimal revenue of the system. In this paper, we first experimentally explore the relation between probabilistic routing and weighted round robin load balancing policies. From the experiment a similar behavior is found between these two policies, which makes it possible to assign the weights according to the routing probability estimated from queueing theoretic heuristic and optimization algorithms studied in the literature. We focus in particular on algorithms based on closed queueing networks for multi-class workloads, which can be used to describe application with service level agreements differentiated across users. We also compare the efficiency of queueing theoretic methods with simple heuristics that do not require to specify a stochastic model of the application. Results indicate that queueing theoretical algorithms yield significantly better results than than routings proportional to the VM capacity with respect to throughput maximization.